Remote handling systems for the ISAC and ARIEL high-power fission and spallation ISOL target facilities at TRIUMF
Bibliographic record
Abstract
TRIUMF, Canada’s particle accelerator centre, is constructing a new high-power ISOL (Isotope Separation On-Line) facility called ARIEL (Advanced Rare IsotopE Laboratory). Thick porous targets will be bombarded with up to 48 kW of 480 MeV protons from TRIUMF’s cyclotron, or up to 100 kW of 30 MeV electrons from a new e-linac, to produce short-lived radioisotopes for a variety of applications, including nuclear astrophysics, fundamental nuclear structure and nuclear medicine. For efficient release of radioisotopes, the targets are heated to temperatures approaching 2000 °C, and are exposed to GSv/h level radiation fields resulting from intended fissions and spallations. Due to these conditions, the operational life for each target is only about five weeks, calling for frequent remote target exchanges to limit downtime. A few days after irradiation, the targets have a residual radiation field producing a dose rate on the order of 10 Sv/h at 1 m, requiring several years of decay prior to shipment to a national disposal facility. TRIUMF is installing new remote handling infrastructure dedicated to ARIEL, including hot cells and a remote handling crane. The system design applies learnings from multiple existing facilities, including CERN-ISOLDE, GANIL-SPIRAL II as well as TRIUMF’s ISAC (Isotope Separator and ACcelerator).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.043 | 0.019 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".